System and method for using biometrics to predict and select music preferences

ABSTRACT

Systems and methods for using biometrics to select music preference are provided. A system for using biometrics to select music preferences for a user in a vehicle, comprises a music selection module electrically coupled to at least one biometric sensor in the vehicle, wherein the at least one biometric sensor senses a characteristic of the user and outputs data for the sensed characteristic to the music selection module, and wherein the music selection module selects a music selection for the user based on the sensed characteristic data, and a controller module electrically coupled to the music selection module to control playing of the music selection, wherein the controller module receives an output including the music selection from the music selection module.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. application Ser. No.13/679,407, filed on Nov. 16, 2012, which is a Continuation of U.S.application Ser. No. 13/660,510, filed on Oct. 25, 2012, the disclosuresof which are incorporated herein in their entireties by reference.

TECHNICAL FIELD

The field generally relates to systems and methods for using biometricsto predict and select music preferences and, in particular, systems andmethods for using biometrics to predict and select music preferences fora vehicle occupant.

BACKGROUND

When listening to music, such as while driving in a car or at work,users often change to a different radio station or song if a musicalselection does not appeal to them. Known systems, such as PANDORA,RHAPSODY and LAST.FM attempt to organize or utilize users' musicpreferences based on, for example, artists, reviews, and songs in users'libraries. With these known systems, explicit information and feedbackfrom the users in the form of, for example, selection of preferredartists and songs, and purchase of songs, is required to develop andwork with users' preferences and improve the listening experience.

Other known systems access a user's electronic collection of audio orvideo recordings to determine an audio/video preference profile for auser, and/or receive preference information from a user and adapt atleast a portion of the preference information into at least one theme.

SUMMARY

In general, exemplary embodiments of the invention include systems andmethods for using biometrics to select music preferences and, inparticular, systems and methods for using biometrics to predict andselect music preferences for a vehicle occupant.

According to an exemplary embodiment of the present invention, a systemfor using biometrics to select music preferences for a user in avehicle, comprises a music selection module electrically coupled to atleast one biometric sensor in the vehicle, wherein the at least onebiometric sensor senses a characteristic of the user and outputs datafor the sensed characteristic to the music selection module, and whereinthe music selection module selects a music selection for the user basedon the sensed characteristic data, and a controller module electricallycoupled to the music selection module to control playing of the musicselection, wherein the controller module receives an output includingthe music selection from the music selection module.

The characteristic may be at least one of a pulse rate, a bodytemperature, a facial expression and a body movement. The biometricsensor may comprise at least one of a pressure sensor, a motion sensor,a pulse rate sensor, a temperature sensor and a camera.

The music selection module may receive environmental data and selectsthe music selection for the user based on the sensed characteristic dataand the environmental data. The environmental data may be at least oneof a day of a week, a time of the day, weather, season and drivingroute.

A database of previously analyzed biometric data may be electricallycoupled to the music selection module, wherein the music selectionmodule selects the music selection for the user based on the sensedcharacteristic data and the previously analyzed biometric data.

The music selection module may comprise at least one of a biometricsensor interpreter, a gesture analyzer, and an emotion classifier.

The music selection module may comprise a searching module capable ofsearching at least one of a database and FM frequencies for a matchingor similar music selection to the selected music selection. Thesearching module may search for music selections according to a musicclassification pattern.

The music selection module may comprise a filter including a patternrecognition tool to analyze models of biometric data applied on thefilter, wherein the filter outputs a prediction of music selections forthe user based on the models. The models may include personalized usermodels developed during a training phase. The models may be developedfrom reaction variant patterns, wherein the reaction variant patternsare based on linear combinations of other patterns. The patternrecognition tool may further analyze environmental data to output theprediction of music selections based on the environmental data.

According to an exemplary embodiment of the present invention, a methodfor using biometrics to select music preferences for a user in avehicle, comprises monitoring the vehicle for an input from a biometricsensor, determining whether any inputs from the biometric sensor havebeen detected, determining whether a music selection is being played,interpreting the input from the biometric sensor to predict whether thesensory input indicates satisfaction with the music selection beingplayed, and selecting another music selection other than the musicselection being played based on the interpretation of the input from thebiometric sensor.

The method may further comprise playing the other music selection inplace of the music selection being played if it is determined that thesensory input does not indicate satisfaction with the music selectionbeing played.

The method may further comprise playing the other music selection afterthe music selection being played ends if it is determined that thesensory input indicates satisfaction with the music selection beingplayed.

The input from the biometric sensor may comprise at least one of a pulserate, a body temperature, a facial expression and a body movement.

The method may further comprise analyzing environmental data andselecting the other music selection based on the interpretation of theinput from the biometric sensor and the environmental data. Theenvironmental data may be at least one of a day of a week, a time of theday, weather, season and driving route.

The method may further comprise selecting the other music selectionbased on the interpretation of the input from the biometric sensor andpreviously analyzed biometric data.

The method may further comprise analyzing models of biometric dataapplied on a filter, wherein the filter outputs a prediction of musicselections for the user based on the models. The models may be developedfrom reaction variant patterns based on linear combinations of otherpatterns.

According to an embodiment of the present invention, an article ofmanufacture comprises a computer readable storage medium comprisingprogram code tangibly embodied thereon, which when executed by acomputer, performs method steps for using biometrics to select musicpreferences for a user in a vehicle, the method steps comprisingmonitoring the vehicle for an input from a biometric sensor, determiningwhether any inputs from the biometric sensor have been detected,interpreting the input from the biometric sensor, analyzingenvironmental data, and selecting a music selection based on theinterpretation of the input from the biometric sensor and theenvironmental data.

According to an embodiment of the present invention, an apparatus forusing biometrics to select music preferences for a user in a vehicle,comprises a memory, and a processor coupled to the memory and configuredto execute code stored in the memory for monitoring the vehicle for aninput from a plurality of biometric sensors, determining whether anyinputs from the biometric sensors have been detected, interpreting theinputs from the biometric sensors to develop models of biometric data,applying and analyzing the models of the biometric data on a filter, andoutputting a prediction of music selections for the user based on themodels.

These and other exemplary embodiments of the invention will be describedor become apparent from the following detailed description of exemplaryembodiments, which is to be read in connection with the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the present invention will be described belowin more detail, with reference to the accompanying drawings, of which:

FIG. 1 is a high-level diagram of a system for predicting and selectingmusic preferences according to an exemplary embodiment of the invention.

FIG. 2 is high-level diagram showing detail of a song selection modulein system for predicting and selecting music preferences according to anexemplary embodiment of the invention.

FIG. 3 is high-level diagram illustrating an operation of a statisticalfilter in a system for predicting and selecting music preferencesaccording to an exemplary embodiment of the invention.

FIG. 4 is high-level diagram showing detail of a statistical filter in asystem for predicting and selecting music preferences according to anexemplary embodiment of the invention.

FIG. 5 is flow diagram of a method for predicting and selecting musicpreferences according to an exemplary embodiment of the invention.

FIG. 6 illustrates a computer system in accordance with which one ormore components/steps of the techniques of the invention may beimplemented, according to an exemplary embodiment of the invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Exemplary embodiments of the invention will now be discussed in furtherdetail with regard to systems and methods for using biometrics to selectmusic preferences and, in particular, systems and methods for usingbiometrics to select music preferences for a vehicle occupant. Thisinvention may, however, be embodied in many different forms and shouldnot be construed as limited to the embodiments set forth herein.

Users behave differently when they like or dislike a musical selection,such as a song, and this behavior can be quantitatively measured throughdifferent biometric measurements. Biometric measurements measure, forexample, physiological and behavioral characteristics including, but notlimited to, an individual's voice, movements, gestures, expressions,posture, etc.

Embodiments of the present invention use biometric information todetermine when a user likes or dislikes a musical selection. As aresult, music can be automatically adjusted based on the user's behaviorand mood.

For example, according to an embodiment, when in a vehicle, such as acar, bus, truck, boat, airplane, etc., a song selection module (e.g.,105 in FIG. 1) can use information such as movements on the vehicleseats, pressure, temperature and humidity levels on the steering wheelor armrests, facial expressions, gestures, etc., to determine whether auser, such as the driver or a passenger in the vehicle, likes ordislikes musical selections. Users tend to provide implicit physicalfeedbacks when they like something, and these feedbacks can be measuredusing biometric sensors. Such feedbacks include, but are not limited toshaking one's body with the beat of the music and tapping a steeringwheel when a user/driver likes a song.

Various biometric sensors are placed around the user(s) to measurequantities including, but not limited to, voice level, pulse rate, handtemperature, facial expressions (e.g., happy/sad) and body movements,including, but not limited to, tapping one's hands or feet, snappingfingers and bobbing one's head. Sensors may include, but are not limitedto, pressure, motion, pulse rate and/or temperature sensors, located,for example, on the steering wheel, seat and/or any other surface in thevehicle, microphones and video and/or still image cameras located, forexample, in or on the dashboard, visor, rearview mirror, window, radioand/or at a user's feet.

According to an embodiment, an initial training phase begins where thesystem learns what musical selections a user likes and dislikes undervarious biometric measurements, by using, for example, a machinelearning algorithm. In accordance with an embodiment of the presentinvention, data concerning what a user likes and dislikes can be inputto a machine learning algorithm as labels and processed using patternrecognition techniques. Like what is shown in FIG. 3, the data analyzedaccording to the machine learning algorithm during the initial trainingphase may include, but is not limited to, data from one or more sensors,cameras and microphones, previously analyzed or categorized dataresults, the current music/song stream and environment data. Inaccordance with an embodiment of the present invention, models andhistory data for analysis by a statistical filter 300 can be developedduring the initial training phase.

According to an embodiment, once learning is completed, the systemcontinuously measures biometric data from the user to determine whatmusical selections to play. For example, if the system detects thelistener(s) are unhappy with the current selection, the system will notethe data, and try to find a better song to play. In another example, ifa user starts dancing in place to a certain song, the system will notethe user's activity and update its models and history data that can beinput into a statistical filter 300. In accordance with an embodiment,if the system puts on a musical selection that it has determined that auser will like, and a user changes or cancels the musical selectionselected by the system, the system can recognize that a potentialmistake has been made, as indicated by the user changing the song. Themachine learning algorithm then updates its parameters with newinformation as to what biometric measurements indicate a wanted orunwanted musical selection.

According to an embodiment, in a modeling method for collecting andanalyzing users' biometric information, reaction variant patterns areused to predict users' music preferences. Reaction variant patterns arevariations in patterns detected from users, and are based on linearcombinations of other patterns.

More specifically, according to an embodiment, there is an existing setof reaction patterns (for example, tap, nod, and jump) that have beenalready categorized with some preferred music labels/titles. Thesereaction patterns are denoted as a matrix H with columns that representthe patterns. When new patterns are detected, for example, dancemovements, which are not categorized under any preferred labels/titles,the new patterns are denoted as y. Then, a sparse solution in x isdetermined according to the linear equation y=Hx to find out whichlabels/titles most overlap with nonzero entries in x, where x is aselector. In other words, the uncategorized reaction patterns arerelated to the closest categorized reaction patterns, and theuncategorized reaction patterns are then categorized under the samelabels/titles as their closest categorized reaction patterns.

The patterns can include, but are not limited to, gestures, bodymeasurements (pulse, humidity, face expressions, emotions, voice, etc.).According to an embodiment, a modality sensor in a vehicle can be usedto detect variations in patterns. In an example, a dance movementpattern, including the combination of tapping of fingers and an excitedvoice, can be defined as a reaction variant pattern.

According to embodiments of the present invention, reaction variantpatterns are used to build a prediction model that predicts if a userlikes a song. Based on such a prediction, a song that is similar to thecurrent preferences can be played. Referring to FIG. 3, these predictionmodels can be input into the statistical filter 300 for processing.

According to an embodiment of the present invention, the biometricinformation that is collected is not limited to the driver, andbiometric information of one or more passengers inside the vehicle canbe used to predict the music that will be liked in the vehicle. Forexample, the system can analyze the biometric information of multipleindividuals to develop their preferences and search for any overlappingmusical selections that would be liked by the group. It is to beunderstood, that the collection of the biometric information from thepassengers can be performed in a same or similar matter as that of thedriver.

According to an embodiment, the system is also capable of learning asong classification pattern, for example, based on artist or genre, fromreading biometric data from the driver and/or passengers and relatingthe read data to one or more classification patterns. The system usesthe song classification pattern or variable to choose songs from a songlist and/or from scanning radio stations looking for preferred songsfalling within the classification pattern.

In accordance with an embodiment, features other than biometricmeasurements, including, but not limited to environmental factors, suchas the day of the week, time of day, weather, season and driving route,are factored into the learning algorithm when creating the personalizedmodels. The environmental information can be gathered using, forexample, a camera, a global positioning system (GPS), internal calendarsand/or wireless networks.

The system can also detect conversations between occupants in thevehicle and lower the volume of a musical selection if it detects thatthe occupants in a vehicle are in a conversation mode, and bringing thevolume back up as the conversation ceases.

The system can also be used outside of a vehicle, by, for example,bicycle riders (with portable music systems like iPods® or iPhones®) andin other situations when users are running, walking or sitting andlistening their portable music systems. According to an embodiment, thesystem is capable of playing music that is already available on aportable music system, and can also search a frequency range, such asthe FM range, to find songs that will match a user's preferences basedon the biometric and other data.

Referring to FIG. 1, according to an embodiment of the presentinvention, the system 10 includes a vehicle 100, one or more users 101,a speaker 102, a musical selection scheduling controller 103, a databaseof musical selections 104 (e.g., songs and other music), a musicalselection module 105, a camera 106, a microphone 107, other biometricsensors 108, a history database 109 including, for example, gestures,songs and other music, and an environmental input module 110, whichgathers information on environmental factors, such as the day of theweek, time of day, weather, season and driving route. The musicalselection module 105 can be for example, a server, software, aprocessor, or any other module for performing the task of combining andanalyzing the various factors and finding a musical selection to beplayed. The musical selection module can be located in the vehicle 100,or positioned remote from the vehicle 100, and accessed via a wirelessnetwork.

As can be seen in FIG. 1, the musical selection module 105 usesavailable data from a camera 106, microphone 107, other biometricsensors 108, a history database 109 of previously analyzed data of auser and/or the environment 110 to select, categorize and/or predict amusical selection that a user 101 likes. The musical selection module105 can also take into account current or previously played musicinformation from a scheduling controller 103, and draw music selectionsfrom a database 104 to be played via one or more speakers 102 in thevehicle 100. The musical selection module 105 also provides selectedmusic data to the scheduling controller 103 for scheduling playing of aselection, to the database of musical selections 104 to update, forexample, a song list, and to the history database 109 for future use andanalysis. The scheduling controller 103 can be electrically coupled to asound system in the vehicle 100 including speakers 102.

Referring to FIG. 2, according to an embodiment of the presentinvention, the musical selection module 105 includes, but is not limitedto, an interpreter of biometric sensors 200, analyzer of gestures 201,an emotional classifier 202, a predictor of desirable musical selectionsfor a user 203, and a system for searching for musical selections 204.According to an embodiment, the interpreter of biometric sensors 200,the analyzer of gestures 201, and the emotional classifier 202 interpretthe data from any one of the camera 106, microphone 107 or otherbiometric sensors 108 to determine a mood and/or reaction of the user101. These elements 200, 201 and 202 may use the historical data fromdatabase 109 to make the determination. Once the determination of moodand/or reaction of the user is made, the predictor of desirable musicalselections 203 predicts songs and music that a user 101 will like andnot like based on the determination, as well as information fromdatabases 104 and 109. The system for searching 204 then searches formatching or similar songs in the database 104 or on FM frequencies. Asstated above, according to an embodiment, the system 204 may searchbased on a song classification pattern, such as artist or genre.

In an embodiment, the interpreter of biometric sensors 200 iselectrically coupled to the analyzer of gestures 201, and the analyzerof gestures is electrically coupled to the emotional classifier 202.With this configuration, the interpreter of biometric sensors 200 cansend an input result (e.g., a conclusion as to a type of gesture made)to the analyzer of gestures 201, which can analyze a gesture todetermine, for example, whether the gesture indicates satisfaction ordissatisfaction. The emotional classifier 202 can receive an inputresult from the analyzer of gestures 201, and classify an emotionalstate of the user as, for example, happy or sad. With the predictor ofdesirable musical selections 203 electrically coupled to the emotionalclassifier, the emotional classification can then be used to predict adesired musical selection based on the emotional classification. Thesystem for searching 204, which is electrically coupled to the predictor203, then searches for matching or similar songs in the database 104 oron FM frequencies based on the output of the predictor 203.

Referring to FIG. 3, the musical selection module includes a statisticalfilter 300. The statistical filter 300 receives input in the form ofgesture models 302, emotional models 303, biometric data 304, historydata 306, the current music/song stream 307 and environment data 308,and outputs a prediction of desirable musical selections 301 and othermusical selection data 305, which may include, for example, undesirablesongs. According to an embodiment, the models 302, 303 include thepersonalized models developed during the initial training phase, andfrom the reaction variant patterns as discussed above. In addition, themodels 302, 303 can also include generalized models based on generalhuman behavior. The history data 306 includes, for example, priordeterminations of liked musical selections based on biometric andenvironmental data that has been previously collected and analyzed. Thestatistical filter 300 also takes into account present biometric andenvironmental data 304, 308, as well as the instant music stream 307.

Referring to FIG. 4, the statistical filter 300 uses one or more of thefollowing pattern recognition tools: deep belief network 400, exemplarbased methods 401, Gaussian mixture models 402, and conditional randomfields 403. It is to be understood that the statistical filter is notlimited to these pattern recognition tools, and other availableprobabilistic generative models, statistical modeling and/or probabilityfunctions may be used.

Referring to FIG. 5, a method for predicting and selecting musicpreferences according to an exemplary embodiment of the invention isshown. At block 500, the system monitors for sensory input. At block501, a determination is made as to whether any inputs from the camera106, microphone 107 or other biometric sensors 108 have been detected.If the answer is no at block 501, the system continues to monitor forsensory input. If the answer is yes at block 501, then the systemqueries whether a musical selection is being played at block 502. If theanswer is no at block 502, the system continues to monitor for sensoryinput. If the answer is yes at block 502, then the system interprets thesensory input at block 503, and queries at block 504 whether the sensoryinput indicates satisfaction or happiness with the musical selection. Ifthe answer is no at block 504, the system categorizes the musicalselection as one which the user dislikes at block 505, finds a newmusical selection which has previously been determined as liked by theuser at block 506, and plays the new musical selection in place of thecurrent musical selection at block 507, at which point, the methodreturns to block 500. If the answer is yes at block 504, the systemcategorizes the musical selection as one which the user likes at block508, finds a new musical selection which has previously been determinedas liked by the user and is similar to the current musical selection atblock 509, and plays the new musical selection after the current musicalselection has ended at block 510, at which point, the method returns toblock 500. According to an embodiment, the environmental factors mayalso be factored into the analysis at, for example, at blocks 503 and504.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, apparatus, method, or computerprogram product. Accordingly, aspects of the present invention may takethe form of an entirely hardware embodiment, an entirely softwareembodiment (including firmware, resident software, micro-code, etc.) oran embodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

FIGS. 1, 2, 3, 4 and 5 illustrate the architecture, functionality, andoperation of possible implementations of systems, methods, and computerprogram products according to various embodiments of the presentinvention. In this regard, each block in a flowchart or a block diagrammay represent a module, segment, or portion of code, which comprises oneor more executable instructions for implementing the specified logicalfunction(s). It should also be noted that, in some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagram and/or flowchart illustration, and combinations of blocksin the block diagram and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

One or more embodiments can make use of software running on ageneral-purpose computer or workstation. With reference to FIG. 6, in acomputing node 610 there is a computer system/server 612, which isoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 612 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 612 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 612 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 6, computer system/server 612 in computing node 610 isshown in the form of a general-purpose computing device. The componentsof computer system/server 612 may include, but are not limited to, oneor more processors or processing units 616, a system memory 628, and abus 618 that couples various system components including system memory628 to processor 616.

The bus 618 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

The computer system/server 612 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 612, and it includes both volatileand non-volatile media, removable and non-removable media.

The system memory 628 can include computer system readable media in theform of volatile memory, such as random access memory (RAM) 630 and/orcache memory 632. The computer system/server 612 may further includeother removable/non-removable, volatile/nonvolatile computer systemstorage media. By way of example only, storage system 634 can beprovided for reading from and writing to a non-removable, non-volatilemagnetic media (not shown and typically called a “hard drive”). Althoughnot shown, a magnetic disk drive for reading from and writing to aremovable, non-volatile magnetic disk (e.g., a “floppy disk”), and anoptical disk drive for reading from or writing to a removable,non-volatile optical disk such as a CD-ROM, DVD-ROM or other opticalmedia can be provided. In such instances, each can be connected to thebus 618 by one or more data media interfaces. As depicted and describedherein, the memory 628 may include at least one program product having aset (e.g., at least one) of program modules that are configured to carryout the functions of embodiments of the invention. A program/utility640, having a set (at least one) of program modules 642, may be storedin memory 628 by way of example, and not limitation, as well as anoperating system, one or more application programs, other programmodules, and program data. Each of the operating system, one or moreapplication programs, other program modules, and program data or somecombination thereof, may include an implementation of a networkingenvironment. Program modules 642 generally carry out the functionsand/or methodologies of embodiments of the invention as describedherein.

Computer system/server 612 may also communicate with one or moreexternal devices 614 such as a keyboard, a pointing device, a display624, etc., one or more devices that enable a user to interact withcomputer system/server 612, and/or any devices (e.g., network card,modem, etc.) that enable computer system/server 612 to communicate withone or more other computing devices. Such communication can occur viaInput/Output (I/O) interfaces 622. Still yet, computer system/server 612can communicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 620. As depicted, network adapter 620communicates with the other components of computer system/server 612 viabus 618. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 612. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Although illustrative embodiments of the present invention have beendescribed herein with reference to the accompanying drawings, it is tobe understood that the invention is not limited to those preciseembodiments, and that various other changes and modifications may bemade by one skilled in the art without departing from the scope orspirit of the invention.

We claim:
 1. A method for using biometrics to select music preferencesfor a user in a vehicle, the method comprising: monitoring the vehiclefor an input from a biometric sensor; determining whether any inputsfrom the biometric sensor have been detected; determining whether amusic selection is being played; interpreting the input from thebiometric sensor to predict whether the sensory input indicatessatisfaction with the music selection being played; selecting anothermusic selection other than the music selection being played based on theinterpretation of the input from the biometric sensor; categorizing themusic selection being played as one the user likes or dislikes based onthe interpretation of the input from the biometric sensor; and analyzingmodels of biometric data applied on a filter; wherein the filter outputsa prediction of music selections for the user based on the models; andwherein the models are developed from reaction variant patterns, and themethod further comprises: respectively categorizing existing reactionvariant patterns under different music selections; detectinguncategorized reaction variant patterns; determining to whichcategorized reaction variant patterns the uncategorized reaction variantpatterns are closest; and categorizing the uncategorized reactionvariant patterns under the same music selections as their closestcategorized reaction variant patterns.
 2. The method of claim 1, furthercomprising playing the other music selection in place of the musicselection being played if it is determined that the sensory inputindicates dissatisfaction with the music selection being played, and thecategorization of the music selection being played as one the userdislikes.
 3. The method of claim 1, further comprising playing the othermusic selection after the music selection being played ends in responseto a determination that the sensory input indicates satisfaction withthe music selection being played, and the categorization of the musicselection being played as one the user likes.
 4. The method of claim 1,wherein the input from the biometric sensor comprises at least one of apulse rate, a body temperature, a facial expression and a body movement.5. The method of claim 1, wherein the biometric sensor comprises atleast one of a pressure sensor, a motion sensor, a pulse rate sensor, atemperature sensor and a camera.
 6. The method of claim 1, furthercomprising analyzing environmental data and selecting the other musicselection based on the interpretation of the input from the biometricsensor and the environmental data.
 7. The method of claim 6, wherein theenvironmental data is at least one of a day of a week, a time of theday, weather, season and driving route.
 8. The method of claim 1,further comprising selecting the other music selection based on theinterpretation of the input from the biometric sensor and previouslyanalyzed biometric data.
 9. The method of claim 1, further comprisingsearching at least one of a database and FM frequencies for a matchingor similar music selection to the other music selection.
 10. The methodof claim 9, wherein the searching is performed according to a musicclassification pattern.
 11. The method of claim 1, wherein the reactionvariant patterns are based on linear combinations of other patterns. 12.The method of claim 1, wherein the models include personalized usermodels developed during a training phase.
 13. The method of claim 1,further comprising: denoting the existing reaction variant patterns as amatrix, wherein the determining to which categorized reaction variantpatterns the uncategorized reaction variant patterns are closestcomprises determining which music selections most overlap with nonzeroentries when solving a linear equation based on the matrix.
 14. Themethod of claim 1, wherein the input from the biometric sensor comprisesat least one of a facial expression and a body movement, and is capturedby at least one of a motion sensor and a camera.
 15. The method of claim1, further comprising: detecting a conversation between the user andanother occupant in the vehicle; decreasing a volume of the musicselection being played based on the detection of the conversation; andincreasing the volume of the music selection being played to a pointprior to the decreasing upon detecting ceasing of the conversation. 16.A method for using biometrics to select music preferences for a user ina vehicle, the method comprising: monitoring the vehicle for an inputfrom a biometric sensor; determining whether any inputs from thebiometric sensor have been detected; determining whether a musicselection is being played; interpreting the input from the biometricsensor to predict whether the sensory input indicates satisfaction withthe music selection being played; selecting another music selectionother than the music selection being played based on the interpretationof the input from the biometric sensor; and categorizing the musicselection being played as one the user likes or dislikes based on theinterpretation of the input from the biometric sensor, wherein: thevehicle includes another user; the input from the biometric sensorcomprises biometric information of the user and the other user; and themethod further comprises determining overlapping music selections likedby both the user and the other user.
 17. A method for using biometricsto select music preferences for a user in a vehicle, the methodcomprising: monitoring the vehicle for an input from a biometric sensor;determining whether any inputs from the biometric sensor have beendetected; interpreting the input from the biometric sensor; analyzingenvironmental data; categorizing a music selection being played as onethe user likes or dislikes based on the interpretation of the input fromthe biometric sensor; and selecting another music selection based on theinterpretation of the input from the biometric sensor and theenvironmental data, wherein: the vehicle includes another user; theinput from the biometric sensor comprises biometric information of theuser and the other user; and the method further comprises determiningoverlapping music selections liked by both the user and the other user.18. A method for using biometrics to select music preferences for a userin a vehicle, the method comprising: monitoring the vehicle for an inputfrom a plurality of biometric sensors; determining whether any inputsfrom the biometric sensors have been detected; interpreting the inputsfrom the biometric sensors to develop models of biometric data; applyingand analyzing the models of the biometric data on a filter; categorizingmusic selections as those the user likes or dislikes based on themodels; outputting a prediction of music selections for the user basedon the models, wherein the models are developed from reaction variantpatterns; respectively categorizing existing reaction variant patternsunder different music selections; detecting uncategorized reactionvariant patterns; determining to which categorized reaction variantpatterns the uncategorized reaction variant patterns are closest; andcategorizing the uncategorized reaction variant patterns under the samemusic selections as their closest categorized reaction variant patterns.